def test_vertex_coloring(self):
# test that method works with diagonal entries
assert_equal(vertex_coloring(np.eye(1)), [0])
assert_equal(vertex_coloring(np.eye(3)), [0, 0, 0])
assert_equal(sorted(vertex_coloring(np.ones((3, 3)))), [0, 1, 2])
for method in ['MIS', 'JP', 'LDF']:
for G in self.cases:
c = vertex_coloring(G, method=method)
assert_is_vertex_coloring(G, c)
def test_vertex_coloring(self):
# test that method works with diagonal entries
assert_equal(vertex_coloring(eye(1)), [0])
assert_equal(vertex_coloring(eye(3)), [0, 0, 0])
assert_equal(sorted(vertex_coloring(ones((3, 3)))), [0, 1, 2])
for method in ['MIS', 'JP', 'LDF']:
for G in self.cases:
c = vertex_coloring(G, method=method)
assert_is_vertex_coloring(G, c)
def preprocess(S, coloring_method=None):
"""Preprocess splitting functions.
Parameters
----------
S : csr_matrix
Strength of connection matrix
method : string
Algorithm used to compute the vertex coloring:
* 'MIS' - Maximal Independent Set
* 'JP' - Jones-Plassmann (parallel)
* 'LDF' - Largest-Degree-First (parallel)
Returns
-------
weights: ndarray
Weights from a graph coloring of G
S : csr_matrix
Strength matrix with ones
T : csr_matrix
transpose of S
G : csr_matrix
union of S and T
Notes
-----
Performs the following operations:
- Checks input strength of connection matrix S
- Replaces S.data with ones
- Creates T = S.T in CSR format
- Creates G = S union T in CSR format
- Creates random weights
- Augments weights with graph coloring (if use_color == True)
"""
if not isspmatrix_csr(S):
raise TypeError('expected csr_matrix')
if S.shape[0] != S.shape[1]:
raise ValueError('expected square matrix, shape=%s' % (S.shape, ))
N = S.shape[0]
S = csr_matrix((np.ones(S.nnz, dtype='int8'), S.indices, S.indptr),
shape=(N, N))
T = S.T.tocsr() # transpose S for efficient column access
G = S + T # form graph (must be symmetric)
G.data[:] = 1
weights = np.ravel(T.sum(axis=1)) # initial weights
# weights -= T.diagonal() # discount self loops
if coloring_method is None:
weights = weights + sp.rand(len(weights))
else:
coloring = vertex_coloring(G, coloring_method)
num_colors = coloring.max() + 1
weights = weights + (sp.rand(len(weights)) + coloring) / num_colors
return (weights, G, S, T)
def preprocess(S, coloring_method=None):
"""Preprocess splitting functions.
Parameters
----------
S : csr_matrix
Strength of connection matrix
method : string
Algorithm used to compute the vertex coloring:
* 'MIS' - Maximal Independent Set
* 'JP' - Jones-Plassmann (parallel)
* 'LDF' - Largest-Degree-First (parallel)
Returns
-------
weights: ndarray
Weights from a graph coloring of G
S : csr_matrix
Strength matrix with ones
T : csr_matrix
transpose of S
G : csr_matrix
union of S and T
Notes
-----
Performs the following operations:
- Checks input strength of connection matrix S
- Replaces S.data with ones
- Creates T = S.T in CSR format
- Creates G = S union T in CSR format
- Creates random weights
- Augments weights with graph coloring (if use_color == True)
"""
if not isspmatrix_csr(S):
raise TypeError('expected csr_matrix')
if S.shape[0] != S.shape[1]:
raise ValueError('expected square matrix, shape=%s' % (S.shape,))
N = S.shape[0]
S = csr_matrix((np.ones(S.nnz, dtype='int8'), S.indices, S.indptr),
shape=(N, N))
T = S.T.tocsr() # transpose S for efficient column access
G = S + T # form graph (must be symmetric)
G.data[:] = 1
weights = np.ravel(T.sum(axis=1)) # initial weights
# weights -= T.diagonal() # discount self loops
if coloring_method is None:
weights = weights + sp.rand(len(weights))
else:
coloring = vertex_coloring(G, coloring_method)
num_colors = coloring.max() + 1
weights = weights + (sp.rand(len(weights)) + coloring)/num_colors
return (weights, G, S, T)
def test_vertex_coloring(self):
for method in ['MIS', 'JP', 'LDF']:
for G in self.cases:
c = vertex_coloring(G, method=method)
assert_is_vertex_coloring(G, c)
def test_vertex_coloring(self):
for method in ['MIS', 'JP', 'LDF']:
for G in self.cases:
c = vertex_coloring(G, method=method)
assert_is_vertex_coloring(G, c)
